Related Art
Medical research and medical diagnosis often require the analysis of vasts amount of sample data from sample points that are each measured for various constituents. For example, medical research can require profiling of a sample pool of blood samples of numerous individuals participating in a study. This can include determining the activity of enzymes in cells. For example, see Lucas et al. (U.S. Pat. Nos. 5,871,946 and 5,698,411) incorporated herein by reference. Each individual can be considered a constituent of the sample pool and various parameters of the sample of the individual's blood sample can be measured as attributes of the sample. The attributes can be the expressions of elements of the blood such as red blood cells, white bloods, enzyme levels and minerals, etc. Additionally, the attributes can be characteristics of cells such as a count of a particular receptor site on a white blood cell or a certain RNA or DNA sequence.
Another facet of medical research and medical diagnosis includes using tissue or blood samples to diagnose disease and broadly assess the state of a person's health, etc. In this situation, one blood sample can be analyzed for numerous constituents such as various enzymes, white blood cells, red blood cells, nutrient levels, enzyme levels and the various characteristics of the cells, etc. Each constituent can then be characterized based upon its attributes.
To analyze the sample data of both an individual's blood and a pool of samples from numerous individuals, biplots can be used. Biplots provide a two-dimensional graphical display of sample data for multivariate sample data such as a sample pool of blood samples or sample data of the various constituents and attributes of an individual blood sample. FIG. 1 provides an example of a biplot. The biplot for either the individual sample or the sample pool are based on principal component analysis (PCA). For a discussion of PCA, see Leary et al., “New Methods for Detection Analysis and Isolation of Rare Cell Populations,” SPIE vol. 2678, pgs. 240–253, 1996, incorporated herein by reference.
FIG. 1 depicts one example of a biplot according to the prior art. The biplot of FIG. 1 represents multivariate data by placing vectors A, B, C, and D in multidimensional space on a principal component plane for the first principal component P1. Each vector A, B, C, and D has a length proportional to the variance of data corresponding to the particular constituent A, B, C, and D or the expression of a particular constituent. The angle between the vectors corresponds to the degree of disparity between the attributes of the vectors A, B, C, and D. For a discussion of biplots, see Leary et al.
FIG. 2 depicts another type of prior art that indicates the presence of three constituents having various percentages of a cell phenotype. The “+” and “−” signs indicate the presence or absence, respectively, of the constituents on a cell. As depicted by FIG. 2, a sample of 8% of the cells contains none of three constituents, and a sample of 12% of the cells is positive for one constituent while the other two constituents are absent.
Other fields of study have similarly complex multivariate data analysis needs. For example, criminal research can require developing a profile of the psychological characteristics of criminals that commit particular crimes. Also, geology can require analyzing the attributes of a myriad of soil and groundwater samples corresponding to many wells or borings in a research site.
Technology is continually advancing and allowing computers and processors to process increasing amounts of complex data. Additionally, it has become possible to use artificial intelligence to determine if data results are favorable or unfavorable. As the ability to analyze vast amounts of data has increased and changed biplots have become insufficient tools for representing data. For example, biplots do not present data in a format that allows a user to readily read and understand the results including unfavorable sample data and/or favorable sample data. Additionally, biplots are limited to a two dimensional representation of the data which can present a distorted view of the angle between the vectors.
Previously, it has been a problem to represent multi-parametric data in a single display whether it is in a two or three-dimensional representation. Another shortcoming of the prior art has been its failure to inform the user of the meaning of the data. The present invention overcomes these and other problems by creating an iconized data display which gives the ability to distill complex data into a quick snapshot image, which reveals unique parameters, associated with clinical relevance.